Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2011.02.028
Title: Importance sampling as a variational approximation
Authors: Nott, D.J. 
Li, J. 
Fielding, M. 
Keywords: Bayesian computation
Importance sampling
Variational approximation
Issue Date: Aug-2011
Citation: Nott, D.J., Li, J., Fielding, M. (2011-08). Importance sampling as a variational approximation. Statistics and Probability Letters 81 (8) : 1052-1055. ScholarBank@NUS Repository. https://doi.org/10.1016/j.spl.2011.02.028
Abstract: There is a well-recognized need to develop Bayesian computational methodologies that scale well to large data sets. Recent attempts to develop such methodology have often focused on two approaches-variational approximation and advanced importance sampling methods. This note shows how importance sampling can be viewed as a variational approximation, achieving a pleasing conceptual unification of the two points of view. We consider a particle representation of a distribution as defining a certain parametric model and show how the optimal approximation (in the sense of minimization of a Kullback-Leibler divergence) leads to importance sampling type rules. This new way of looking at importance sampling has the potential to generate new algorithms by the consideration of deterministic choices of particles in particle representations of distributions. © 2011 Elsevier B.V.
Source Title: Statistics and Probability Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/105175
ISSN: 01677152
DOI: 10.1016/j.spl.2011.02.028
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

WEB OF SCIENCETM
Citations

1
checked on Oct 9, 2020

Page view(s)

99
checked on Oct 10, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.